Statistik

Joint research seminar of the Chair of Statistics and the Chair of Econometrics

Content of this course:

Regression analysis is one of the most developed and commonly used methods in the statistical toolbox. This course gives an introduction to the vast field of regression modelling techniques that extend the classical linear regression model. The course presents the foundations of regression analysis and highlights its application, interpretation, and underlying assumptions.

The topics of this course include a primer on the classical linear regression model, regression models for non-normal responses, and non-parametric smoothing techniques to handle non-linear covariate effects. Data examples illustrate these methods. The lecture is accompanied by an exercise that will show how to apply these approaches, implement the methods using statistical software packages, and interpret the results.

Data protection and copyright:

Some lecture and exercise class might also be available online via Zoom video conferences. It is prohibited to record these video conferences in any way (video, audio, screenshots, etc.). The content of the course, including all provided material, is intellectual property of the respective lecturer (unless declared otherwise) and protected by copyright. Only students enrolled in the Moodle course “Generalized Regression (SS21)” are allowed to use it. In particular, the publication (also partial), duplication, dissemination, and editing of our material (including video conferences) are prohibited. Any violation can be prosecuted.

All students enrolled in the Moodle course pledge themselves to observe the data protection and copyright rules and to use the material (including video conferences) only in the context of their studies individually.

By enrolling in the Moodle course “Generalized Regression (SS21)”, you agree to these data protection and copyright rules.